MedCo builds and text-enriches a medical knowledge graph using LLMs and EHR data, then fuses text and graph signals via joint LoRA-tuned LLaMA and heterogeneous GNN training to improve EHR clinical predictions on MIMIC datasets.
Reasoning-enhanced healthcare predictions with knowledge graph community retrieval.arXiv preprint arXiv:2410.04585, 2024
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EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
K2K framework enables internal memory retrieval in LLMs for healthcare outcome prediction, achieving state-of-the-art results on four benchmarks.
SciDC turns flexible scientific knowledge into standardized decoding constraints via LLMs, delivering a 12% average accuracy gain over vanilla generation on tasks including formulation design, tumor diagnosis, and retrosynthesis.
COTCAgent combines a code-executing statistics adapter, a weighted knowledge-base chain-of-thought layer, and constrained inquiry to reach 90.47% and 70.41% top-1 accuracy on two medical datasets.
citing papers explorer
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Text-Attributed Knowledge Graph Enrichment with Large Language Models for Medical Concept Representation
MedCo builds and text-enriches a medical knowledge graph using LLMs and EHR data, then fuses text and graph signals via joint LoRA-tuned LLaMA and heterogeneous GNN training to improve EHR clinical predictions on MIMIC datasets.
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EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning
EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
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Efficient and Effective Internal Memory Retrieval for LLM-Based Healthcare Prediction
K2K framework enables internal memory retrieval in LLMs for healthcare outcome prediction, achieving state-of-the-art results on four benchmarks.
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Scientific Knowledge-driven Decoding Constraints Improving the Reliability of LLMs
SciDC turns flexible scientific knowledge into standardized decoding constraints via LLMs, delivering a 12% average accuracy gain over vanilla generation on tasks including formulation design, tumor diagnosis, and retrosynthesis.
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COTCAgent: Preventive Consultation via Probabilistic Chain-of-Thought Completion
COTCAgent combines a code-executing statistics adapter, a weighted knowledge-base chain-of-thought layer, and constrained inquiry to reach 90.47% and 70.41% top-1 accuracy on two medical datasets.